Handwritten character recognition using background analysis

Guido Tascini; Paolo Puliti; Primo Zingaretti

doi:10.1117/12.143613

14 April 1993 Handwritten character recognition using background analysis

Guido Tascini, Paolo Puliti, Primo Zingaretti

Proceedings Volume 1906, Character Recognition Technologies; (1993) https://doi.org/10.1117/12.143613
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the `acquisition' module, the `binarization' module, and the `core' module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and `profile' analysis, `cut' analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as `profiles' and `cuts;' a `profile' is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a `cut' is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the `profiles' and `cuts,' and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 X 32 pixel character is of about 97% after the first iteration, and of over 98% after the second iteration.

Citation Download Citation

Guido Tascini, Paolo Puliti, and Primo Zingaretti "Handwritten character recognition using background analysis", Proc. SPIE 1906, Character Recognition Technologies, (14 April 1993); https://doi.org/10.1117/12.143613

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KEYWORDS

Optical character recognition

Image segmentation

Magnetic resonance imaging

Feature extraction

Analytical research

Image resolution

Binary data

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